Updated random_forest.py

app.py

@@ -10,7 +10,7 @@ from models.espcn import espcn_upscale
 from models.sr_gan import srgan_upscale
 
 # ✅ New import for Random Forest Super Resolution
-from models.random_forest_sr import random_forest_upscale
+from CV_Project.models.random_forest_sr import random_forest_upscale
 
 lancros_page = gr.Interface(
     fn=upsample_lancros,

models/random_forest_sr.py

@@ -1,15 +1,16 @@
 import numpy as np
-from skimage import transform, util
+from skimage import transform, util, color
 from sklearn.ensemble import RandomForestRegressor
 from skimage.util import view_as_windows
 from PIL import Image
 
 # CONFIGURATION
-PATCH_SIZE = (5, 5)
+PATCH_SIZE = (3, 3)
 STEP = 1
-N_ESTIMATORS = 50
-MAX_DEPTH = 20
+N_ESTIMATORS = 10
+MAX_DEPTH = 10
 SCALE_FACTOR = 2
+SAMPLE_PATCHES = 10000  # Controls speed/accuracy trade-off
 
 def extract_patches(img, patch_size, step):
     patches = view_as_windows(img, patch_size, step)
@@ -17,7 +18,11 @@ def extract_patches(img, patch_size, step):
     return patches.reshape(h * w, -1)
 
 def train_rf(X, y):
-    rf = RandomForestRegressor(n_estimators=N_ESTIMATORS, max_depth=MAX_DEPTH, n_jobs=-1)
+    rf = RandomForestRegressor(
+        n_estimators=N_ESTIMATORS,
+        max_depth=MAX_DEPTH,
+        n_jobs=-1
+    )
     rf.fit(X, y)
     return rf
 
@@ -53,12 +58,18 @@ def random_forest_upscale(pil_img: Image.Image) -> Image.Image:
 
     for c in range(img.shape[2]):
         channel = img[:, :, c]
-        hr_channel = transform.resize(channel, hr_shape)
-        lr_channel = transform.resize(hr_channel, (hr_shape[0] // SCALE_FACTOR, hr_shape[1] // SCALE_FACTOR))
-        lr_channel_up = transform.resize(lr_channel, hr_shape)
+        hr_channel = transform.resize(channel, hr_shape, anti_aliasing=True)
+        lr_channel = transform.resize(hr_channel, (hr_shape[0] // SCALE_FACTOR, hr_shape[1] // SCALE_FACTOR), anti_aliasing=True)
+        lr_channel_up = transform.resize(lr_channel, hr_shape, anti_aliasing=True)
 
         X = extract_patches(lr_channel_up, PATCH_SIZE, STEP)
         y = extract_patches(hr_channel, PATCH_SIZE, STEP)
+
+        if X.shape[0] > SAMPLE_PATCHES:
+            idx = np.random.choice(X.shape[0], SAMPLE_PATCHES, replace=False)
+            X = X[idx]
+            y = y[idx]
+
         rf_model = train_rf(X, y)
         sr = predict_and_reconstruct(rf_model, lr_channel_up, PATCH_SIZE, STEP, hr_shape)
         sr_channels.append(sr)

models/random_forest_sr1.py

@@ -0,0 +1,68 @@
+import numpy as np
+from skimage import transform, util
+from sklearn.ensemble import RandomForestRegressor
+from skimage.util import view_as_windows
+from PIL import Image
+
+# CONFIGURATION
+PATCH_SIZE = (5, 5)
+STEP = 1
+N_ESTIMATORS = 50
+MAX_DEPTH = 20
+SCALE_FACTOR = 2
+
+def extract_patches(img, patch_size, step):
+    patches = view_as_windows(img, patch_size, step)
+    h, w = patches.shape[:2]
+    return patches.reshape(h * w, -1)
+
+def train_rf(X, y):
+    rf = RandomForestRegressor(n_estimators=N_ESTIMATORS, max_depth=MAX_DEPTH, n_jobs=-1)
+    rf.fit(X, y)
+    return rf
+
+def predict_and_reconstruct(model, lr_img, patch_size, step, out_shape):
+    lr_patches = extract_patches(lr_img, patch_size, step)
+    preds = model.predict(lr_patches)
+
+    patch_h, patch_w = patch_size
+    img_h = (lr_img.shape[0] - patch_h) // step + 1
+    img_w = (lr_img.shape[1] - patch_w) // step + 1
+
+    result = np.zeros(out_shape)
+    weight = np.zeros(out_shape)
+
+    idx = 0
+    for i in range(img_h):
+        for j in range(img_w):
+            patch = preds[idx].reshape(patch_h, patch_w)
+            result[i*step:i*step+patch_h, j*step:j*step+patch_w] += patch
+            weight[i*step:i*step+patch_h, j*step:j*step+patch_w] += 1
+            idx += 1
+
+    weight[weight == 0] = 1
+    return result / weight
+
+def random_forest_upscale(pil_img: Image.Image) -> Image.Image:
+    img = np.array(pil_img) / 255.0  # Normalize
+    if img.ndim == 2:
+        img = np.expand_dims(img, axis=-1)
+
+    hr_shape = (img.shape[0] * SCALE_FACTOR, img.shape[1] * SCALE_FACTOR)
+    sr_channels = []
+
+    for c in range(img.shape[2]):
+        channel = img[:, :, c]
+        hr_channel = transform.resize(channel, hr_shape)
+        lr_channel = transform.resize(hr_channel, (hr_shape[0] // SCALE_FACTOR, hr_shape[1] // SCALE_FACTOR))
+        lr_channel_up = transform.resize(lr_channel, hr_shape)
+
+        X = extract_patches(lr_channel_up, PATCH_SIZE, STEP)
+        y = extract_patches(hr_channel, PATCH_SIZE, STEP)
+        rf_model = train_rf(X, y)
+        sr = predict_and_reconstruct(rf_model, lr_channel_up, PATCH_SIZE, STEP, hr_shape)
+        sr_channels.append(sr)
+
+    sr_image = np.stack(sr_channels, axis=-1)
+    sr_image = np.clip(sr_image * 255, 0, 255).astype(np.uint8)
+    return Image.fromarray(sr_image)